KickStart Startup Trend Prediction

#UNDERSTAND THE BUSINESS
#It is important for entrepreneurs to know how likely a project is going to be "sucessful" if launched on Kickstarter
#possible factors include not meeting minimum funding goal or the category of the product/service. 
#This project analyzes statistics to find correlation among 13 features of a project 
#to predict a likelihood of a project to be succesful on Kickstarter - that is fully funded 

#Import data set from url: https://www.kaggle.com/kemical/kickstarter-projects/data
#data in 2018
url1 <- "/Users/tamvu/DS4100/Kickstarter Predict/Startup Prediction/kickstarter-projects/ks-projects-201801.csv"
raw_df <- as.data.frame(read.csv(url1), na.strings = c("", "NA", "undefined"))
#original size is 350k+ which is too big data. I will take a sample 30% 
raw_df <- raw_df[sample(nrow(raw_df) * 0.4), ]

library(RMySQL)
#CONNECT TO MYSQL DATABASE
awsURL <- "mypersonalinstance.cwoxgifwds2r.us-east-1.rds.amazonaws.com"
con <- dbConnect(MySQL(),
                 user = "vut1307",
                 password = "Vuminhtam1307",
                 host = awsURL,
                 port = 3306,
                 dbname = "kickstarterprojects")
#import data into the database
dbWriteTable(con, name = 'dataset', raw_df, overwrite = TRUE)

[1] TRUE

-- RETRIEVE DATA FROM DATABASE
SELECT * FROM dataset;

#UNDERSTAND THE DATA
#There are over 350k entries recorded since April 2009 to December 2017. Total 15 variables 
#Since ID and names are indentity of the data rather than a factor that determines any outcome, 
#one of the variable is the actual outcome of the data: sucessful/failed/canceled/live. 
#the rest are features that can contribute to the state 
#DEFINE OUTCOME "sucessful": 
#states of a project recorded into the dataset
df <- raw_df
table(df$state)

  canceled     failed       live successful  suspended  undefined 
     15548      78797       1127      53875        707       1410 

#I am only taking into account projects that are not live or suspended (because there is no result to it yet if so) 
#projects that are not canceled (because the result is not one of the features in the dataset)
#only projects that is determined with final state (either failed/sucessful)

#EXPLORATORY DATA ANALYSIS: analyze to find important factors that indicate the outcome of successful or failed projects 
#percentage of sucessful vs failed
n <- nrow(subset(df, df$state == "successful" | df$state == "failed"))
mytable <- as.data.frame(table(df$state) / n * 100)
slices <- c(59.33, 40.67) 
lbls <- paste(c( "failed", "successful"), slices, "%")
pie(slices, labels = lbls, main="Pie Chart of Sucessful projects")

#RETRIEVE DATA TO EXPLORE 
#What is the trendiest product being proposed on Kickstarter in the sample?
dbGetQuery(con, '
SELECT main_category AS most_popular_category, 
AVG(backers) AS num_backers, 
AVG(usd_goal_real) AS avg_goal,
AVG(usd_pledged_real) AS avg_pledged,
(SELECT country FROM dataset WHERE state = "successful" GROUP BY country ORDER BY count(*) DESC LIMIT 1) AS most_by_country,
COUNT(*) AS number_proposed FROM dataset 
  GROUP BY main_category
  ORDER BY number_proposed DESC LIMIT 1;
')

Decimal MySQL column 1 imported as numeric

#What is the trendiest product being proposed on Kickstarter that is sucessful? 
#What is the trendiest product being proposed on Kickstarter that is failed?
dbGetQuery(con, '
SELECT  * FROM
(SELECT state, main_category AS most_popular_category, 
AVG(backers) AS num_backers, 
AVG(usd_goal_real) AS avg_goal,
AVG(usd_pledged_real) AS avg_pledged, COUNT(*) AS number_proposed 
FROM dataset WHERE state = "successful"
GROUP BY main_category ORDER BY number_proposed DESC LIMIT 1) good
UNION
(SELECT state, main_category AS most_popular_category, 
AVG(backers) AS num_backers, 
AVG(usd_goal_real) AS avg_goal,
AVG(usd_pledged_real) AS avg_pledged, COUNT(*) AS number_proposed 
FROM dataset WHERE state = "failed"
GROUP BY main_category ORDER BY number_proposed DESC LIMIT 1);
')

Decimal MySQL column 2 imported as numeric

#as we can see from queries the most popular projects is Film and Video but they are most likely to fail.

#What is a good ammount of goal for a project to be less likely to fail? MUCH LOWER THAN THE FAILED PROJECTS 
#Compare the successful projects compared to failed projects with same amount of backers but lower goals than average bad goals?
dbGetQuery(con, '
SELECT D1.main_category, D1.goal AS good_goal, D.avg_fail_goal FROM dataset D1
JOIN (SELECT *, AVG(goal) AS avg_fail_goal FROM dataset D2 WHERE D2.state = "failed") D 
ON D1.backers = D.backers AND D1.goal < D.avg_fail_goal
WHERE  D1.state = "successful"
ORDER BY good_goal DESC;
')

-- Get the projects that is active till most recently.
SELECT * FROM dataset WHERE state = "live" AND YEAR(deadline) = 2018 ORDER BY month(deadline) DESC;

#DATA PREPARATION: CLEANING, IMPUTATION
#raw data has 113 entries.
#MISSING VALUES: disregard all entries that have any missing values of any variable 
#which results in 112k entries with total 15 features
df <- na.omit(df)

#CONSTRUCT DATA: calculate the duration of the project
#I want to disregard the effect of actual time because the result will depend on the events have actually happen at the time. 
df$duration <- as.numeric(as.Date(df$deadline) - as.Date(df$launched))
#SHAPE DATA:DUMMY CODE for the catergories
#1 - Art, 2 - Comics, 3 - Crafts, 4 - Dance, 5 - Design, 6 - Fashion, 7 - Film & Video,
#8 - Food, 9 - Games, 10 - Journalism, 11 - Music, 12 - Photography, 13 - Publishing, 14 - Technology
df$encode_category <- as.numeric(df$main_category)
#DUMMY CODE FOR state: successful = 1, failed = 0
df$encode_state <- as.numeric(df$state)/2 - 1
#DUMMY CODE FOR country
df$encode_country <- as.numeric(df$country)

#CLEAN DATA to sucessful/failed only.
#I am only taking into account projects that are not live or suspended (because there is no result to it yet if so) 
#projects that are not canceled (because the result is not one of the features in the dataset)
#only projects that is determined with final state (either failed/sucessful)
# results in 331465 entries
df <- subset(df, df$state == "failed" | df$state == "successful")
#OMIT IRRELEVANT variables: ID and names. results in 13 variables: 12 features and 1 that is the state
#I also omit (sub)category and only consider main_category to be more general
#Omit time variables: deadline and launched
#Money variable like goals and pledged I am choosing the columns in USD only so ommit currency and general goal and pledged columns
nonvars <- c("ID","name", "category", "goal", "launched", "deadline", "pledged", "state", "currency", "main_category", "country", "usd.pledged")
df <- df[,!(names(df) %in% nonvars)]

#EXPLORE DATA: CORRELATION ANALYSIS
cor(df$usd_goal_real, df$encode_state)

[1] -0.02301614

cor(df$backers, df$encode_state) 

[1] 0.1327455

cor(df$duration, df$encode_state)

[1] -0.1203274

cor(df$usd_pledged_real, df$encode_state)

[1] 0.1283374

# the sucessful projects has lower funding goals, lower duration, 
# higher number of backers, and higher pledged in USD

#EXPLORATORY PLOTS - DETECT AND REMOVE OUTLIERS 
#function to detetect and remove outliers
#source https://www.r-bloggers.com/identify-describe-plot-and-remove-the-outliers-from-the-dataset/
outlierKD <- function(dt, var, str) {
  var_name <- eval(substitute(var),eval(dt))
  tot <- sum(!is.na(var_name))
  na1 <- sum(is.na(var_name))
  m1 <- mean(var_name, na.rm = T)
  par(mfrow=c(2, 2), oma=c(0,0,3,0))
  boxplot(var_name, main="With outliers")
  hist(var_name, main="With outliers", xlab=NA, ylab=NA)
  outlier <- boxplot.stats(var_name)$out
  mo <- mean(outlier)
  var_name <- ifelse(var_name %in% outlier, NA, var_name)
  boxplot(var_name, main="Without outliers")
  hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
  title(paste("Outlier Check of", str) , outer=TRUE)
  na2 <- sum(is.na(var_name))
  message("Outliers identified: ", na2 - na1, " from ", tot, " observations")
  message("Proportion (%) of outliers: ", (na2 - na1) / tot*100)
  message("Mean of the outliers: ", mo)
  m2 <- mean(var_name, na.rm = T)
  message("Mean without removing outliers: ", m1)
  message("Mean if we remove outliers: ", m2)
  response <- "yes"
  if(response == "y" | response == "yes"){
    dt[as.character(substitute(var))] <- invisible(var_name)
    assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
    message("Outliers successfully removed", "\n")
    return(invisible(dt))
  } else{
    message("Nothing changed", "\n")
    return(invisible(var_name))
  }
}

#remove outliers of number of backers
outlierKD(df,backers, "number of backers")

#remove outliers of pledged number
outlierKD(df,usd_pledged_real, "USD real pledged")

outlierKD(df,usd_goal_real, "goal in USD")

#NORMALIZATION OF DATA
#MIN-MAX NORMALIZATION of backers and duration of fundraising
min_max_norm <- function(data) {
  options(scipen = 999)
  minX <- min(as.numeric(data)) 
  maxX <- max(as.numeric(data))
  data <- (as.numeric(data) - minX)/(maxX - minX)
}
min_max_norm(df$backers)
min_max_norm(df$duration)

#NORMALIZATION OF DATA
#Z-SCORE STANDARDIZATION of goal and pledged
df$usd_goal_real <- scale(df$usd_goal_real)
df$usd_pledged_real <- scale(df$usd_pledged_real)

#DATA MODELING
#Create a stratified sample where you randomly select 70% of successful and failed projects to be part of the validation data set.
df <- na.omit(df)
train_Set <- data.frame()
set.seed(5)
for(i in 0:1) {
  all_of_type_i <- subset(df, df$encode_state == i)
  train_Set <- rbind(train_Set, all_of_type_i[sample(0.7 * nrow(all_of_type_i)),])
}
#The remaining cases will form the training data set. 
test_Set <- df[!(as.numeric(row.names(df)) %in% as.numeric(row.names(train_Set))), ]

#MULTIPLE LINEAR REGRESSION MODEL
lin_model <- lm(train_Set$encode_state ~ ., data = train_Set)
summary(lin_model)

Call:
lm(formula = train_Set$encode_state ~ ., data = train_Set)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3363 -0.2551 -0.1029  0.2750  0.7867 

Coefficients:
                    Estimate  Std. Error  t value             Pr(>|t|)    
(Intercept)       0.26469518  0.00609693   43.415 < 0.0000000000000002 ***
backers           0.00458467  0.00006462   70.946 < 0.0000000000000002 ***
usd_pledged_real  0.17782802  0.00232103   76.616 < 0.0000000000000002 ***
usd_goal_real    -0.18498998  0.00138442 -133.623 < 0.0000000000000002 ***
duration         -0.00196377  0.00009581  -20.497 < 0.0000000000000002 ***
encode_category  -0.00014506  0.00031003   -0.468                 0.64    
encode_country    0.00130859  0.00020254    6.461       0.000000000105 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3309 on 70681 degrees of freedom
Multiple R-squared:  0.5214,    Adjusted R-squared:  0.5214 
F-statistic: 1.283e+04 on 6 and 70681 DF,  p-value: < 0.00000000000000022

#REVISE THE LINEAR REGRESSION
lin_model <- lm(train_Set$encode_state ~ . - encode_category, data = train_Set)
summary(lin_model)

Call:
lm(formula = train_Set$encode_state ~ . - encode_category, data = train_Set)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3368 -0.2551 -0.1029  0.2750  0.7861 

Coefficients:
                    Estimate  Std. Error  t value             Pr(>|t|)    
(Intercept)       0.26351128  0.00554703   47.505 < 0.0000000000000002 ***
backers           0.00458482  0.00006462   70.950 < 0.0000000000000002 ***
usd_pledged_real  0.17782397  0.00232100   76.615 < 0.0000000000000002 ***
usd_goal_real    -0.18502058  0.00138287 -133.795 < 0.0000000000000002 ***
duration         -0.00196573  0.00009571  -20.538 < 0.0000000000000002 ***
encode_country    0.00130951  0.00020253    6.466       0.000000000101 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3309 on 70682 degrees of freedom
Multiple R-squared:  0.5214,    Adjusted R-squared:  0.5214 
F-statistic: 1.54e+04 on 5 and 70682 DF,  p-value: < 0.00000000000000022

#Ideal linear regression model for predicting the state of a project on Kickstarter in this data set 
#include number of backers, USD pledged, USD goal, duration and country. 
#All are significant with P-value < 0.05 

#LOGISTIC REGRESSION MODEL
log_model <- glm(train_Set$encode_state ~ ., data = train_Set)
summary(log_model)

Call:
glm(formula = train_Set$encode_state ~ ., data = train_Set)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3363  -0.2551  -0.1029   0.2750   0.7867  

Coefficients:
                    Estimate  Std. Error  t value             Pr(>|t|)    
(Intercept)       0.26469518  0.00609693   43.415 < 0.0000000000000002 ***
backers           0.00458467  0.00006462   70.946 < 0.0000000000000002 ***
usd_pledged_real  0.17782802  0.00232103   76.616 < 0.0000000000000002 ***
usd_goal_real    -0.18498998  0.00138442 -133.623 < 0.0000000000000002 ***
duration         -0.00196377  0.00009581  -20.497 < 0.0000000000000002 ***
encode_category  -0.00014506  0.00031003   -0.468                 0.64    
encode_country    0.00130859  0.00020254    6.461       0.000000000105 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.1094887)

    Null deviance: 16170.3  on 70687  degrees of freedom
Residual deviance:  7738.8  on 70681  degrees of freedom
AIC: 44256

Number of Fisher Scoring iterations: 2

#REVISE LOGISTIC REGRESSION MODEL
log_model <- glm(train_Set$encode_state ~ . - encode_category, data = train_Set)
summary(log_model)

Call:
glm(formula = train_Set$encode_state ~ . - encode_category, data = train_Set)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3368  -0.2551  -0.1029   0.2750   0.7861  

Coefficients:
                    Estimate  Std. Error  t value             Pr(>|t|)    
(Intercept)       0.26351128  0.00554703   47.505 < 0.0000000000000002 ***
backers           0.00458482  0.00006462   70.950 < 0.0000000000000002 ***
usd_pledged_real  0.17782397  0.00232100   76.615 < 0.0000000000000002 ***
usd_goal_real    -0.18502058  0.00138287 -133.795 < 0.0000000000000002 ***
duration         -0.00196573  0.00009571  -20.538 < 0.0000000000000002 ***
encode_country    0.00130951  0.00020253    6.466       0.000000000101 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.1094875)

    Null deviance: 16170.3  on 70687  degrees of freedom
Residual deviance:  7738.8  on 70682  degrees of freedom
AIC: 44254

Number of Fisher Scoring iterations: 2

#Ideal multiple regression model for predicting outcome of project in this data set include number of backers, USD pledged, USD goal, duration and country. All are significant with P-value < 0.05 

#From both regression models, category is not a significant idicator of the outcome - which was my hypothesis when I started the project. A protential reason is that the category is only significant regarding the events/trend of the time of the project. For example waste-free trend will elevate a chance of success for a environmental-friendly product like beewax wraper (Bee's Wrap). 

library('class')
#TUNING OF THE K-NN MODEL. 
#Determine an optimal k by trying all values from 5 through 15 for k-NN algorithm against the cases in the validation data set. 
#Source code: https://www.r-bloggers.com/using-knn-classifier-to-predict-whether-the-price-of-stock-will-increase/
min <- 2
max <- 10
accuracy <- rep(0, max-min+1) #initialize with 0 accuracy
k <- min:max
for(x in k){
  prediction <- knn(train_Set, test_Set, train_Set$encode_state, k = x)
  accuracy[x] <- mean(prediction == test_Set$encode_state) * 100 #calculate accuracy
}
#What is the optimal k, i.e., the k that results in the best accuracy? Plot k versus accuracy.
plot(k, accuracy[min:max], type = 'b', main = "Accuracy by k", xlab = "accuracy (%)")

#Most accurate k = 11. REVISE AND BUILD THE K-NN MODEL WITH K = 11
library('class')
knn_model <- knn(train = train_Set, test = test_Set, train_Set$encode_state, k = 3)
table(knn_model, test_Set$encode_state)

         
knn_model     0     1
        0 13823   451
        1   847  7633

# What is the percentage of correct prediction of sucessful projects?
knn_accuracy <- mean(knn_model == test_Set$encode_state) * 100

#helper function to calculate the MAD. linear and logistic regression model has same amount of coefficients
predict_value <- function(data, model, i) {
  return(model$coefficients[[1]]
         + data$backers[i] * model$coefficients[[2]]
         + data$usd.pledged[i] * model$coefficients[[3]]
         + data$usd_pledged_real[i] * model$coefficients[[4]]
         + data$duration[i] * model$coefficients[[5]]
         + data$encode_country[i] * model$coefficients[[6]])
}
MAD <- function(model) {
  sum <- 0
  n <- nrow(df)
  for(i in 1:n) {
    sum <- sum + abs(predict_value(df,model, i) - df$encode_state[i])
  }
  return(sum)
}

#EVALUATE THE FIT OF MODELS
#Linear model: 
sumr_lin <- summary(lin_model)
lin_MAD <- MAD(lin_model)
lin_MSE <- mean(summary(lin_model)$residuals^ 2)
paste("Linear Regression model has R-squared of", sumr_lin$r.squared, "which explains the model fits the training data well; has MAD of", lin_MAD, "which is actually higher than R-squared; has MSE of", lin_MSE)

[1] "Linear Regression model has R-squared of 0.522412900229395 which explains the model fits the training data well; has MAD of  which is actually higher than R-squared; has MSE of 0.109393976331674"

#Logistic model
log_MAD <- MAD(log_model) 
paste("Logistic Regression model has MAD of", log_MAD, "explains the model fits the training data well")

[1] "Logistic Regression model has MAD of  explains the model fits the training data well"

#A function to determine if the model has bias (false positive), e.g predicting a project failed when they actually succeeded
bias <- function(model) {
  prediction <- predict(model, type = "response", newdata = test_Set)
prediction <- ifelse(prediction > 0.5,1,0) #set survival = 1
combine <- data.frame(prediction, test_Set$encode_state)
#filter out all errors the model made
error <- subset(combine, combine$prediction != combine$test_Set.encode_state)
#false positive are the errors with people who are actual survived
falsePositive <- subset(error, error$test_Set.encode_state == 1)
falseNegative <- subset(error, error$test_Set.encode_state == 0)
return(c(nrow(falsePositive)/length(prediction)*100, nrow(falseNegative)/length(prediction)*100))
}

#COMPARE BIAS
falsePos <- bias(log_model)[1] #2%
falseNeg <- bias(log_model)[2] #10%
falsePos_lin <- bias(lin_model)[1] #2%
falseNeg_lin <- bias(lin_model)[2] #10%
paste("Logistic Regression Model and Linear Regression Model has more false positive. Hence they are bias towards predicting a project fail")

[1] "Logistic Regression Model and Linear Regression Model has more false positive. Hence they are bias towards predicting a project fail"

#function get_accuracy: takes in a model and output the percentage of how correct the prediction 
get_accuracy <- function(name, model) {
  prediction <- predict(model, type = "response", newdata = test_Set)
  prediction <- ifelse(prediction > 0.5,1,0) #set survival = 1
#and determine its prediction accuracy (as a percentage correct).
#by comparing the prediction to the test data set survival result
correctPrediction <- mean(prediction == test_Set$encode_state)
return(paste('Prediction Accuracy of', name, 'is', correctPrediction * 100, '%'))
}

#COMPARE THE ACCURACY OF MODELS
paste(get_accuracy("Multiple Linear Regression Model", lin_model), "with R-squared is 0.51")

[1] "Prediction Accuracy of Multiple Linear Regression Model is 86.7495824909906 % with R-squared is 0.51"

get_accuracy("Logistic Regression Model", log_model)

[1] "Prediction Accuracy of Logistic Regression Model is 86.7495824909906 %"

paste("Prediction Accuracy of k-NN Model with k=3 is", knn_accuracy, "%")

[1] "Prediction Accuracy of k-NN Model with k=3 is 94.2955084820251 %"

paste("The regression models have very similar close results. Though linear regression model is has MAD higher than R-squared, both fit the training data well. Compared to k-NN, they are best predictors of a succesful project though has 10% false positive bias.")

[1] "The regression models have very similar close results. Though linear regression model is has MAD higher than R-squared, both fit the training data well. Compared to k-NN, they are best predictors of a succesful project though has 10% false positive bias."

#PREDICT THE LIVE PROJECTS USING MULTIPLE LINEAR REGRESSION: 
#predict the active projects that were live until last month
#format the data frame
active_df <- subset(raw_df, raw_df$ID %in% active_df$ID)
active_df$duration <- as.numeric(as.Date(active_df$deadline) - as.Date(active_df$launched))
active_df$encode_category <- as.numeric(active_df$main_category)
active_df$encode_country <- as.numeric(active_df$country)
#predict successfulness using linear regression
pred_values <- c()
for(i in 1:nrow(active_df)) {
  pred_values <- c(pred_values, predict_value(active_df, lin_model, i))
}
pred_values <- ifelse(pred_values > 0.5,1,0) 
active_df$predict_state <- pred_values
active_df

#PREDICT USING K-NN 
active_df_knn <- active_df[,!(names(active_df) %in% nonvars)]
#predict successfulness using k-nn regression
knn_pred <- knn(train = train_Set, test = active_df_knn, cl = train_Set$encode_state, k = 3)
active_df_knn$knn_pred <- knn_pred

#get the average data of linear results
n <- nrow(active_df)
good_proj <- subset(active_df, active_df$predict_state == 1)
percentage_succesful <- nrow(good_proj) / n * 100
country <- names(which.max(table(good_proj$country)))
avg_backers <- mean(good_proj$backers)
avg_goal <- mean(good_proj$usd_goal_real)
avg_pledged <- mean(good_proj$usd_pledged_real)
avg_duration <- mean(good_proj$duration)
#INTERPRETATION OF RESULTS: 
paste("LINEAR REGRESSION PREDICTS:")

[1] "LINEAR REGRESSION PREDICTS:"

paste("On the prediction results of active projects,", percentage_succesful, "% are successful.")

[1] "On the prediction results of active projects, 0.888888888888889 % are successful."

paste("From my average calculation, in order to be successful project on Kickstarter, the creator should aim to launch in ", country, "with a good amount of backers of at least", ceiling(avg_backers), "setting a goal lower than $", avg_goal, "and a duration of fund-raising as short as", floor(avg_duration), "days")

[1] "From my average calculation, in order to be successful project on Kickstarter, the creator should aim to launch in  US with a good amount of backers of at least 4 setting a goal lower than $ 18906.488 and a duration of fund-raising as short as 45 days"

#get the average data of k-nn results
n <- nrow(active_df)
good_proj <- subset(active_df, active_df_knn$knn_pred == 1)
percentage_succesful <- nrow(good_proj) / n * 100
country <- names(which.max(table(good_proj$country)))
avg_backers <- mean(good_proj$backers)
avg_goal <- mean(good_proj$usd_goal_real)
avg_pledged <- mean(good_proj$usd_pledged_real)
avg_duration <- mean(good_proj$duration)
#INTERPRETATION OF RESULTS: 
paste("k-NN with k=3 PREDICTS:")

[1] "k-NN with k=3 PREDICTS:"

paste("On the prediction results of active projects,", percentage_succesful, "% are successful.")

[1] "On the prediction results of active projects, 2.57777777777778 % are successful."

paste("From my average calculation, in order to be successful project on Kickstarter, the creator should aim to launch in ", country, "with a good amount of backers of at least", ceiling(avg_backers), "setting a goal lower than $", avg_goal, "and a duration of fund-raising as short as", floor(avg_duration), "days")

[1] "From my average calculation, in order to be successful project on Kickstarter, the creator should aim to launch in  US with a good amount of backers of at least 1412 setting a goal lower than $ 8159.78379310345 and a duration of fund-raising as short as 39 days"